Cargando…
Resting state fMRI connectivity analysis as a tool for detection of abnormalities in five different cognitive networks of the brain in Multiple Sclerosis patients
OBJECTIVES: Cognitive dysfunction is present in at least half of patients with Multiple Sclerosis. The purpose of this study was to examine functional connectivity abnormalities in patients with multiple sclerosis (MS) using resting state fMRI (rsfMRI). METHODS: Conventional MRI, rsfMRI and diffusio...
Autores principales: | , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5697978/ https://www.ncbi.nlm.nih.gov/pubmed/29170718 http://dx.doi.org/10.15761/CCRR.1000S1001 |
_version_ | 1783280706118483968 |
---|---|
author | Nejad-Davarani, Siamak P. Chopp, Michael Peltier, Scott Li, Lian Davoodi-Bojd, Esmaeil Lu, Mei Bagher-Ebadian, Hassan Budaj, John Gallagher, David Ding, Yue Hearshen, David Jiang, Quan Cerghet, Mirela |
author_facet | Nejad-Davarani, Siamak P. Chopp, Michael Peltier, Scott Li, Lian Davoodi-Bojd, Esmaeil Lu, Mei Bagher-Ebadian, Hassan Budaj, John Gallagher, David Ding, Yue Hearshen, David Jiang, Quan Cerghet, Mirela |
author_sort | Nejad-Davarani, Siamak P. |
collection | PubMed |
description | OBJECTIVES: Cognitive dysfunction is present in at least half of patients with Multiple Sclerosis. The purpose of this study was to examine functional connectivity abnormalities in patients with multiple sclerosis (MS) using resting state fMRI (rsfMRI). METHODS: Conventional MRI, rsfMRI and diffusion tensor imaging (DTI) data was acquired from 10 patients with relapsing-remitting multiple sclerosis (RRMS) and 20 healthy controls. Cross-correlation of the resting state average signal among the voxels in each brain region of the five cognitive networks: default mode network (DMN), attention, verbal memory, memory, and visuospatial working memory network, was calculated. Voxelwise analyses were used to investigate fractional anisotropy (FA) of white matter tracts. The normalized gray matter (GM), white matter and thalamus volumes were calculated. RESULTS: Compared to controls, significant deficit in MS patients at each of five networks, attention (p=0.026), DMN (p=0.004), verbal memory (p<0.001), memory (p=0.001), visuospatial working memory (p=0.003) was found. Significant reduction (p=0.034) in the normalized GM volume and asymmetry in thalamus volume (p=0.041) was detected in MS patients compared to controls. CONCLUSION: Wide spread of functional abnormalities are present within different cognitive networks in patients with RRMS, suggesting that DMN may not be sufficient for measurement of MS cognitive impairment. Larger and longitudinal studies should ascertain whether rsfMRI of cognitive networks and changes in GM and thalamus volume can be used as tools for assessment of cognition in clinical trials in MS. |
format | Online Article Text |
id | pubmed-5697978 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
record_format | MEDLINE/PubMed |
spelling | pubmed-56979782017-11-21 Resting state fMRI connectivity analysis as a tool for detection of abnormalities in five different cognitive networks of the brain in Multiple Sclerosis patients Nejad-Davarani, Siamak P. Chopp, Michael Peltier, Scott Li, Lian Davoodi-Bojd, Esmaeil Lu, Mei Bagher-Ebadian, Hassan Budaj, John Gallagher, David Ding, Yue Hearshen, David Jiang, Quan Cerghet, Mirela Clin Case Rep Rev Article OBJECTIVES: Cognitive dysfunction is present in at least half of patients with Multiple Sclerosis. The purpose of this study was to examine functional connectivity abnormalities in patients with multiple sclerosis (MS) using resting state fMRI (rsfMRI). METHODS: Conventional MRI, rsfMRI and diffusion tensor imaging (DTI) data was acquired from 10 patients with relapsing-remitting multiple sclerosis (RRMS) and 20 healthy controls. Cross-correlation of the resting state average signal among the voxels in each brain region of the five cognitive networks: default mode network (DMN), attention, verbal memory, memory, and visuospatial working memory network, was calculated. Voxelwise analyses were used to investigate fractional anisotropy (FA) of white matter tracts. The normalized gray matter (GM), white matter and thalamus volumes were calculated. RESULTS: Compared to controls, significant deficit in MS patients at each of five networks, attention (p=0.026), DMN (p=0.004), verbal memory (p<0.001), memory (p=0.001), visuospatial working memory (p=0.003) was found. Significant reduction (p=0.034) in the normalized GM volume and asymmetry in thalamus volume (p=0.041) was detected in MS patients compared to controls. CONCLUSION: Wide spread of functional abnormalities are present within different cognitive networks in patients with RRMS, suggesting that DMN may not be sufficient for measurement of MS cognitive impairment. Larger and longitudinal studies should ascertain whether rsfMRI of cognitive networks and changes in GM and thalamus volume can be used as tools for assessment of cognition in clinical trials in MS. 2016-07-30 2016-09 /pmc/articles/PMC5697978/ /pubmed/29170718 http://dx.doi.org/10.15761/CCRR.1000S1001 Text en http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Article Nejad-Davarani, Siamak P. Chopp, Michael Peltier, Scott Li, Lian Davoodi-Bojd, Esmaeil Lu, Mei Bagher-Ebadian, Hassan Budaj, John Gallagher, David Ding, Yue Hearshen, David Jiang, Quan Cerghet, Mirela Resting state fMRI connectivity analysis as a tool for detection of abnormalities in five different cognitive networks of the brain in Multiple Sclerosis patients |
title | Resting state fMRI connectivity analysis as a tool for detection of abnormalities in five different cognitive networks of the brain in Multiple Sclerosis patients |
title_full | Resting state fMRI connectivity analysis as a tool for detection of abnormalities in five different cognitive networks of the brain in Multiple Sclerosis patients |
title_fullStr | Resting state fMRI connectivity analysis as a tool for detection of abnormalities in five different cognitive networks of the brain in Multiple Sclerosis patients |
title_full_unstemmed | Resting state fMRI connectivity analysis as a tool for detection of abnormalities in five different cognitive networks of the brain in Multiple Sclerosis patients |
title_short | Resting state fMRI connectivity analysis as a tool for detection of abnormalities in five different cognitive networks of the brain in Multiple Sclerosis patients |
title_sort | resting state fmri connectivity analysis as a tool for detection of abnormalities in five different cognitive networks of the brain in multiple sclerosis patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5697978/ https://www.ncbi.nlm.nih.gov/pubmed/29170718 http://dx.doi.org/10.15761/CCRR.1000S1001 |
work_keys_str_mv | AT nejaddavaranisiamakp restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT choppmichael restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT peltierscott restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT lilian restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT davoodibojdesmaeil restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT lumei restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT bagherebadianhassan restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT budajjohn restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT gallagherdavid restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT dingyue restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT hearshendavid restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT jiangquan restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients AT cerghetmirela restingstatefmriconnectivityanalysisasatoolfordetectionofabnormalitiesinfivedifferentcognitivenetworksofthebraininmultiplesclerosispatients |